Validating kfino algorithm (Kalman filter with impulse noised outliers) to filter liveweight outliers produced by the walk-over-weighing (WoW) platform in a large spectrum of farming systems

The liveweight (LW) is conventionally measured using static systems, which require animals to be individually walked onto a set of scales. This process is time consuming, labour intensive, and places stress on both the animals being weighed and the operator; indoors it is relatively easy, but weighing animals outdoors may be a difficult task. To overcome such situation, we developed some years ago a Walk-over-Weighing (WoW) platform which records LW in an automated, non-invasive manner, and have been evaluated with success in a range of conditions. However, a lot of outliers are produced with the WoW, which must be removed to retain only correct data and make coherent interpretations. Standard methods used until now to perform such outliers’ removal still unpractical, time consuming and requires a minimum of mastery by the user. To improve performance of the process we further built an algorithm, so-called Kfino (Kalman Filter with Impulse Noised Outliers) that automatically remove individual daily LW outliers. Then, to vehicle Kfino and for the ease of end-users, we further developed a web application with R-Shiny, so-called ORIOLE. Thus, the objective of this work was to validate the functioning in the field of the whole infrastructure (WoW, Kfino and ORIOLE) in a large spectrum of farming systems for sheep production in Mediterranean settings. A series of trials (n = 8) were conducted at different moments and under different conditions of the experimental farms La Fage and Le Merle (France) and AGRIS (Italy). Animals (n = 920) were either young (ewe-lambs) or adults (ewes) from different breeds (two for meat - Romane and Mérinos d’Arles; and two for dairy - Lacaune and Sarda) and were reared indoors or outdoors (grazing) during several weeks. For each trial, LW outliers generated in raw datasets produced by the WoW were filtered by using a conventional three-steps method (Manual), and the automatic alternative (ORIOLE) proposed here (using Kfino in the ORIOLE interface). Such methods were compared for the parameters i) number of outliers detected, ii) percentage of clean data with respect to the raw dataset, and iii) the time required for running the full filtering and report process. The ability of kfino algorithm was demonstrated and its applicability, through the use of the ORIOLE web app was validated. The final outcomes of the detection and removal of individual LW outliers from the WoW was closely similar with the two methods. The major difference is the practical ease and the time required for the full process (i.e., just minutes using ORIOLE vs. hours with manual). The ORIOLE app, which technical and computational visual improvements still in progress, may be used by a large spectrum of end-users, and provide further and interesting outputs of easy interpretation (including graphical and statistical reports). The whole framework developed in the scope of this work facilitates the development of future early warning systems that could contribute to more efficient monitoring and management of the progression of daily, individual LW of animals, and the related animal health parameters and welfare issues in a large spectrum of conditions.